Instructions to use yadapruk/blip3o-thai-checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yadapruk/blip3o-thai-checkpoint with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yadapruk/blip3o-thai-checkpoint", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
NOTE: This is not a production-ready checkpoint.
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="BLIP3o/BLIP3o-Model-4B",
repo_type="model"
)
Clone the repo (if you haven’t already) and install the environment:
git clone https://github.com/JiuhaiChen/BLIP3o.git
Then run inference with
python inference.py /path/to/checkpoint
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